Currently, implantable medical devices (IMDs) are trending towards providing advanced patient management features that enable health care providers to more closely tailor therapy to meet increasingly particularized patient needs. For instance, based on patient-specific data, health care providers can form a clinical trajectory of projected treatment outcome or generate a wellness indicator to provide both a snapshot reading of patient status and in use in creating a trending analysis. Such patient-specific data can also be used for providing blended sensor optimization, AV interval delay optimization, arrhythmia prediction, and similar IMD-specific programming.
Conventional IMD programming relies primarily upon population-based data. IMD candidate patients are medically evaluated and broadly characterized using well-known sets of classifications, which include, for example, the New York Heart Association (NYHA) classifications, described in E. Braunwald, ed., “Heart Disease—A Textbook of Cardiovascular Medicine,” Ch. 15, pp. 445-470, W.B. Saunders Co. (5th ed. 1997), the disclosure of which is incorporated by reference. Evaluation can include physical stressors, such as described in Ibid. at Ch. 5, pp. 153-176, pharmacological stressors, as well as sensory or autonomic, or metabolic stressors to establish a diagnosis by determining cardiopulmonary functional capacity and to estimate a treatment prognosis.
IMD programming based on population-based data, at best, provides a starting point that requires further refinement to tailor therapy to a recipient patient. Classifications are helpful as an aid to providing an initial set of parameters, but potentially overlook patient-specific features available on a specific IMDs. Additionally, further ad hoc fine tuning during or following surgery is often necessary to eventually arrive at a suitable parameter set. Conversely, patient-specific data, when available, can assist a healthcare provider in defining parameters based on a variety of conditions or situations not routinely factored into parameters selection. For instance, the AV delay in patients indicated for pacing therapy may be initially optimized by maximizing cardiac output at rest, but how the AV delay is programmed to change during exercise is based on population-based data. Historical data from a patient's exercise test conducted prior to the development of Bradycardia indications could be useful for determining the optimal AV delay over a range of physiologically relevant heart rates.
Similarly, IMDs with advanced patient management features generally require learning periods to observe the patient, during which the advanced features are either unavailable or less effective. Such programming changes based solely on empirically-observed data frequently fail to factor in extrinsic predictive markers of disease state, such as family history, current medications, and so forth. Moreover, any reference baseline generated during the learning period post facto may be artificially skewed by the therapeutic effect of the device.
Therefore, there is a need for an approach to preprogramming an IMD or other medical device based on physiological measures and evaluated prior to implantation to pre-seed operational values based on a patient-specific analysis of the physiological measures.